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  <div class="headertitle"><div class="title">NormalizedKernel.h</div></div>
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<a href="_normalized_kernel_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Normalization of a kernel function.</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * </span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * \author      T.Glasmachers, O. Krause, M. Tuma</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \date        2010, 2011</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> *</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * </span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * </span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * </span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * </span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> *</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> */</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#ifndef SHARK_MODELS_KERNELS_NORMALIZED_KERNEL_H</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#define SHARK_MODELS_KERNELS_NORMALIZED_KERNEL_H</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span> </div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_kernel_function_8h.html" title="abstract super class of all kernel functions">shark/Models/Kernels/AbstractKernelFunction.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span> </div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span> </div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment"></span> </div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">/// \brief Normalized version of a kernel function</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">///</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">/// For a positive definite kernel k, the normalized kernel</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">/// \f[ \tilde k(x, y) := \frac{k(x, y)}{\sqrt{k(x, x) \cdot k(y, y)}} \f]</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// is again a positive definite kernel function.</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">/// \ingroup kernels</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> InputType=RealVector&gt;</div>
<div class="foldopen" id="foldopen00051" data-start="{" data-end="};">
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html">   51</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_normalized_kernel.html" title="Normalized version of a kernel function.">NormalizedKernel</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction</a>&lt;InputType&gt;</div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>{</div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">base_type</a>;</div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>    </div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>    <span class="keyword">struct </span>InternalState: <span class="keyword">public</span> <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a>{</div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>        RealMatrix kxy;</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>        RealVector kxx;</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>        RealVector kyy;</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>        </div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>        boost::shared_ptr&lt;State&gt; stateKxy;</div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>        std::vector&lt;boost::shared_ptr&lt;State&gt; &gt; stateKxx;</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>        std::vector&lt;boost::shared_ptr&lt;State&gt; &gt; stateKyy;</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>        </div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>        <span class="keywordtype">void</span> resize(std::size_t sizeX1,std::size_t sizeX2, <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a> <span class="keyword">const</span>* base){</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>            kxy.resize(sizeX1,sizeX2);</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>            kxx.resize(sizeX1);</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>            kyy.resize(sizeX2);</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>            stateKxx.resize(sizeX1);</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>            stateKyy.resize(sizeX2);</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>            stateKxy = base-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a9057a4a71b4d28febb171e09bbd22c07" title="Creates an internal state of the kernel.">createState</a>();</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>            <span class="keywordflow">for</span>(std::size_t i = 0; i != sizeX1;++i){</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>                stateKxx[i] = base-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a9057a4a71b4d28febb171e09bbd22c07" title="Creates an internal state of the kernel.">createState</a>();</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>            } </div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>            <span class="keywordflow">for</span>(std::size_t i = 0; i != sizeX2;++i){</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>                stateKyy[i] = base-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a9057a4a71b4d28febb171e09bbd22c07" title="Creates an internal state of the kernel.">createState</a>();</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>            }</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>        }</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>    };</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#ae982a2e874a2d918533a8f663faf0ecb">   81</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#adbf700c2ece7236c70cef4b88777a733" title="batch input type of the kernel">base_type::BatchInputType</a> <a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#ae982a2e874a2d918533a8f663faf0ecb">BatchInputType</a>;</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#af9c5f353cd3f34beff84c52229cf71d7">   82</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#af923f26f3d015156bb5ac159b302311b" title="Const references to BatchInputType.">base_type::ConstBatchInputReference</a> <a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#af9c5f353cd3f34beff84c52229cf71d7">ConstBatchInputReference</a>;</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#aa82868b403205bf76c3a107cc8788f20">   83</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#a40e365cb5ec7d2776105a4aef4e78df3" title="Const references to InputType.">base_type::ConstInputReference</a> <a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#aa82868b403205bf76c3a107cc8788f20">ConstInputReference</a>;</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>    </div>
<div class="foldopen" id="foldopen00085" data-start="{" data-end="}">
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#ab967fe200113b33b7491e087bf3553dc">   85</a></span>    <a class="code hl_function" href="classshark_1_1_normalized_kernel.html#ab967fe200113b33b7491e087bf3553dc">NormalizedKernel</a>(<a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a>* base) : <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>(base){</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>( base != NULL );</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>        this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_kernel_function.html#aa13e9ab3b8bbad9e1d773468671703e6">m_features</a>|=<a class="code hl_enumvalue" href="classshark_1_1_abstract_kernel_function.html#af54c80ca837961761506e6c2eec15bdea389ad713fc9ba77daf7a89714e5db666" title="does k(x, x) = 1 hold for all inputs x?">base_type::IS_NORMALIZED</a>;</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>        <span class="keywordflow">if</span> ( <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#ac0c799ac75db64200256ed50d34d2411">hasFirstParameterDerivative</a>() ) </div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>            this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_kernel_function.html#aa13e9ab3b8bbad9e1d773468671703e6">m_features</a>|=<a class="code hl_enumvalue" href="classshark_1_1_abstract_kernel_function.html#af54c80ca837961761506e6c2eec15bdead621a9ae065d91a154055a38a7ea72f8" title="is the kernel differentiable w.r.t. its parameters?">base_type::HAS_FIRST_PARAMETER_DERIVATIVE</a>;</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>        <span class="keywordflow">if</span> ( <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a505ca00275044073f08aae949127a76f">hasFirstInputDerivative</a>() ) </div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>            this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_kernel_function.html#aa13e9ab3b8bbad9e1d773468671703e6">m_features</a>|=<a class="code hl_enumvalue" href="classshark_1_1_abstract_kernel_function.html#af54c80ca837961761506e6c2eec15bdeae4bd575af084f862f64bc665cad4c4ec" title="is the kernel differentiable w.r.t. its inputs?">base_type::HAS_FIRST_INPUT_DERIVATIVE</a>;</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    }</div>
</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span><span class="comment"></span> </div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00095" data-start="{" data-end="}">
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#ab8fabb64b228fe65c6e1e1e40597089f">   95</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_normalized_kernel.html#ab8fabb64b228fe65c6e1e1e40597089f" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;NormalizedKernel&lt;&quot;</span> + <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_nameable.html#a9893f99314de30cd472e649c235d0db4" title="returns the name of the object">name</a>() + <span class="stringliteral">&quot;&gt;&quot;</span>; }</div>
</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span> </div>
<div class="foldopen" id="foldopen00098" data-start="{" data-end="}">
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#a1246ed4aa52cb8ce5a2216df17f93426">   98</a></span>    RealVector <a class="code hl_function" href="classshark_1_1_normalized_kernel.html#a1246ed4aa52cb8ce5a2216df17f93426" title="Return the parameter vector.">parameterVector</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#afaa2ba692ab64a0edbff60d7ee6794db" title="Return the parameter vector.">parameterVector</a>();</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>    }</div>
</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span> </div>
<div class="foldopen" id="foldopen00102" data-start="{" data-end="}">
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#a64db356f84025fc719a02644a625c594">  102</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_normalized_kernel.html#a64db356f84025fc719a02644a625c594" title="Set the parameter vector.">setParameterVector</a>(RealVector <span class="keyword">const</span>&amp; newParameters){</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>        <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#ad5e35d1a10ff36fa72ea787baa40e9ad" title="Set the parameter vector.">setParameterVector</a>(newParameters);</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>    }</div>
</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span> </div>
<div class="foldopen" id="foldopen00106" data-start="{" data-end="}">
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#a4d5c9f3d886e3b551f82a8dc6f28dc08">  106</a></span>    std::size_t <a class="code hl_function" href="classshark_1_1_normalized_kernel.html#a4d5c9f3d886e3b551f82a8dc6f28dc08" title="Return the number of parameters.">numberOfParameters</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#aed1e8d1d4dbde387e2f6a25141ed3a20" title="Return the number of parameters.">numberOfParameters</a>();</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>    }</div>
</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>    <span class="comment"></span></div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span><span class="comment">    ///\brief creates the internal state of the kernel</span></div>
<div class="foldopen" id="foldopen00111" data-start="{" data-end="}">
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#aa438ef5e9b9e54da9ba0de32ee02666b">  111</a></span><span class="comment"></span>    boost::shared_ptr&lt;State&gt; <a class="code hl_function" href="classshark_1_1_normalized_kernel.html#aa438ef5e9b9e54da9ba0de32ee02666b" title="creates the internal state of the kernel">createState</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>        InternalState* state = <span class="keyword">new</span> InternalState();</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>        <span class="keywordflow">return</span> boost::shared_ptr&lt;State&gt;(state);</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>    }</div>
</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span><span class="comment"></span> </div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span><span class="comment">    ///evaluates \f$ k(x,y) \f$</span></div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span><span class="comment">    /// calculates</span></div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span><span class="comment">    /// \f[ \tilde k(x, y) := \frac{k(x, y)}{\sqrt{k(x, x) \cdot k(y, y)}} \f]</span></div>
<div class="foldopen" id="foldopen00120" data-start="{" data-end="}">
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#aa6ff3743633aeb4bae2ccac7028cadb7">  120</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_normalized_kernel.html#aa6ff3743633aeb4bae2ccac7028cadb7">eval</a>(<a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#aa82868b403205bf76c3a107cc8788f20">ConstInputReference</a> x1, <a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#aa82868b403205bf76c3a107cc8788f20">ConstInputReference</a> x2)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        <span class="keywordtype">double</span> val = <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(x1, x2);</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        val /= std::sqrt(<a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(x1, x1));</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>        val /= std::sqrt(<a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(x2, x2));</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>        <span class="keywordflow">return</span> val;</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>    }</div>
</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>    </div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>    <span class="comment"></span></div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="comment">    ///evaluates \f$ k(x_i,y_j) \f$ for a batch of inputs x=(x...x_n) and x=(y_1...y_m)</span></div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span><span class="comment">    /// calculates</span></div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span><span class="comment">    /// \f[ \tilde k(x_i,y_j) := \frac{k(x_i,y_j)}{\sqrt{k(x_i,x_i) \cdot k(y_j, y_j)}} \f]</span></div>
<div class="foldopen" id="foldopen00132" data-start="{" data-end="}">
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#a766bf8a238a55a1bdc08886133526a29">  132</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_normalized_kernel.html#a766bf8a238a55a1bdc08886133526a29">eval</a>(<a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#af9c5f353cd3f34beff84c52229cf71d7">ConstBatchInputReference</a> <span class="keyword">const</span>&amp; batchX1, <a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#af9c5f353cd3f34beff84c52229cf71d7">ConstBatchInputReference</a> <span class="keyword">const</span>&amp; batchX2, RealMatrix&amp; result, <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a>&amp; state)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        InternalState&amp; s = state.<a class="code hl_function" href="structshark_1_1_state.html#a9847e65e063245c6b02371c8b84f8da3" title="Safely downcast State to it&#39;s derived type.">toState</a>&lt;InternalState&gt;();</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>        </div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        std::size_t sizeX1 = <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(batchX1);</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        std::size_t sizeX2 = <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(batchX2);</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        s.resize(sizeX1,sizeX2,<a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>);</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        result.resize(sizeX1,sizeX2);</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>        </div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>        <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(batchX1, batchX2,s.kxy, *s.stateKxy);</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>        </div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        </div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        <span class="comment">//possible very slow way to evaluate</span></div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>        <span class="comment">//we need to calculate k(x_i,x_i) and k(y_j,y_j) for every element.</span></div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>        <span class="comment">//we do it by copying the single element in a batch of size 1 and evaluating this. </span></div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>        <span class="comment">//the following could be made easier with an interface like </span></div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>        <span class="comment">//m_base-&gt;eval(batchX1,s.kxx,s.statekxx);</span></div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>        <a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#ae982a2e874a2d918533a8f663faf0ecb">BatchInputType</a> singleBatch = <a class="code hl_struct" href="structshark_1_1_batch.html" title="class which helps using different batch types">Batch&lt;InputType&gt;::createBatch</a>(<a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(batchX1,0));</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>        RealMatrix singleResult(1,1);</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != sizeX1;++i){</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>            <a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(singleBatch,0) = <a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(batchX1,i);</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>            <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(singleBatch,singleBatch,singleResult,*s.stateKxx[i]);</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>            s.kxx[i] = singleResult(0,0);</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        }</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>        </div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>        <span class="keywordflow">for</span>(std::size_t j = 0; j != sizeX2;++j){</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>            <a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(singleBatch,0) = <a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(batchX2,j);</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>            <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(singleBatch,singleBatch,singleResult,*s.stateKyy[j]);</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>            s.kyy[j] = singleResult(0,0);</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>        }</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>        RealVector sqrtKyy=sqrt(s.kyy);</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>        </div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        <span class="comment">//finally calculate the result</span></div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>        <span class="comment">//r(i,j) = k(x_i,x_j)/sqrt(k(x_i,x_i))*sqrt(k(x_j,kx_j))</span></div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>        noalias(result) = s.kxy / outer_prod(sqrt(s.kxx),sqrtKyy);</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>    }</div>
</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>    <span class="comment"></span></div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span><span class="comment">    ///evaluates \f$ k(x,y) \f$ for a batch of inputs</span></div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span><span class="comment">    /// calculates</span></div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span><span class="comment">    /// \f[ \tilde k(x, y) := \frac{k(x, y)}{\sqrt{k(x, x) \cdot k(y, y)}} \f]</span></div>
<div class="foldopen" id="foldopen00172" data-start="{" data-end="}">
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#a1d8a667049dc813d8ce1c7f89f4b7f08">  172</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_normalized_kernel.html#a1d8a667049dc813d8ce1c7f89f4b7f08">eval</a>(<a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#af9c5f353cd3f34beff84c52229cf71d7">ConstBatchInputReference</a> <span class="keyword">const</span>&amp; batchX1, <a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#af9c5f353cd3f34beff84c52229cf71d7">ConstBatchInputReference</a> <span class="keyword">const</span>&amp; batchX2, RealMatrix&amp; result)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        std::size_t sizeX1 = <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(batchX1);</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        std::size_t sizeX2 = <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(batchX2);</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span> </div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>        <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(batchX1, batchX2,result);</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>        </div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>        RealVector sqrtKyy(sizeX2);</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>        <span class="keywordflow">for</span>(std::size_t j = 0; j != sizeX2;++j){</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>            sqrtKyy(j) = std::sqrt(<a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(<a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(batchX2,j),<a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(batchX2,j)));</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>        }</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>        </div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != sizeX1;++i){</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>            <span class="keywordtype">double</span> sqrtKxx = std::sqrt(<a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(<a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(batchX2,i),<a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(batchX2,i)));</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>            noalias(row(result,i)) = sqrtKxx* row(result,i) / sqrtKyy;</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>        }</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>    }</div>
</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span><span class="comment"></span> </div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span><span class="comment">    /// calculates the weighted derivate w.r.t. the parameters \f$ w \f$</span></div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span><span class="comment">    /// The derivative for a single element is calculated as follows:</span></div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span><span class="comment">    ///\f[ \frac{\partial \tilde k_w(x, y)}{\partial w} = \frac{k_w&#39;(x,y)}{\sqrt{k_w(x,x) k_w(y,y)}} - \frac{k_w(x,y) \left(k_w(y,y) k_w&#39;(x,x)+k_w(x,x) k_w&#39;(y,y)\right)}{2 (k_w(x,x) k_w(y,y))^{3/2}} \f]</span></div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span><span class="comment">    /// where \f$ k_w&#39;(x, y) = \partial k_w(x, y) / \partial w \f$.</span></div>
<div class="foldopen" id="foldopen00194" data-start="{" data-end="}">
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#a3e93a1c512126fb0ad7d098dc7c03eb7">  194</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_normalized_kernel.html#a3e93a1c512126fb0ad7d098dc7c03eb7">weightedParameterDerivative</a>(</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>        <a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#af9c5f353cd3f34beff84c52229cf71d7">ConstBatchInputReference</a> <span class="keyword">const</span>&amp; batchX1, </div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>        <a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#af9c5f353cd3f34beff84c52229cf71d7">ConstBatchInputReference</a> <span class="keyword">const</span>&amp; batchX2, </div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>        RealMatrix <span class="keyword">const</span>&amp; coefficients,</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>        <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a> <span class="keyword">const</span>&amp; state, </div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>        RealVector&amp; gradient</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>    )<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>        ensure_size(gradient,<a class="code hl_function" href="classshark_1_1_normalized_kernel.html#a4d5c9f3d886e3b551f82a8dc6f28dc08" title="Return the number of parameters.">numberOfParameters</a>());</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>        InternalState <span class="keyword">const</span>&amp; s = state.<a class="code hl_function" href="structshark_1_1_state.html#a9847e65e063245c6b02371c8b84f8da3" title="Safely downcast State to it&#39;s derived type.">toState</a>&lt;InternalState&gt;();</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>        std::size_t sizeX1 = <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(batchX1);</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>        std::size_t sizeX2 = <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(batchX2);</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>        </div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>        RealMatrix weights = coefficients / sqrt(outer_prod(s.kxx,s.kyy));</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>        </div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>        <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a48557b9834bc06ccb4e005ce441904c8" title="Computes the gradient of the parameters as a weighted sum over the gradient of all elements of the ba...">weightedParameterDerivative</a>(batchX1,batchX2,weights,*s.stateKxy,gradient);</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>        </div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>        noalias(weights) *= s.kxy;</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>        RealVector wx = sum(as_rows(weights)) / (2.0 * s.kxx);</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>        RealVector wy = sum(as_columns(weights)) / (2.0 * s.kyy);</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>        </div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>        <span class="comment">//the following mess could be made easier with an interface like </span></div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>        <span class="comment">//m_base-&gt;weightedParameterDerivative(batchX1,wx,s.statekxx,subGradient);</span></div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>        <span class="comment">//m_base-&gt;weightedParameterDerivative(batchX2,wy,s.statekyy,subGradient);</span></div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>        <span class="comment">//(calculating the weighted parameter derivative of k(x_i,x_i) or (k(y_i,y_i)</span></div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>        RealVector subGradient(gradient.size());</div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>        <a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#ae982a2e874a2d918533a8f663faf0ecb">BatchInputType</a> singleBatch = <a class="code hl_struct" href="structshark_1_1_batch.html" title="class which helps using different batch types">Batch&lt;InputType&gt;::createBatch</a>(<a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(batchX1,0));</div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span>        RealMatrix coeff(1,1);</div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != sizeX1; ++i){</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>            <a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(singleBatch,0) = <a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(batchX1,i);</div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>            coeff(0,0) = wx(i);</div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span>            <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a48557b9834bc06ccb4e005ce441904c8" title="Computes the gradient of the parameters as a weighted sum over the gradient of all elements of the ba...">weightedParameterDerivative</a>(singleBatch,singleBatch,coeff,*s.stateKxx[i],subGradient);</div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>            gradient -= subGradient;</div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span>        }</div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>        <span class="keywordflow">for</span>(std::size_t j = 0; j != sizeX2; ++j){</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>            <a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(singleBatch,0) = <a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(batchX2,j);</div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>            coeff(0,0) = wy(j);</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>            <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a48557b9834bc06ccb4e005ce441904c8" title="Computes the gradient of the parameters as a weighted sum over the gradient of all elements of the ba...">weightedParameterDerivative</a>(singleBatch,singleBatch,coeff,*s.stateKyy[j],subGradient);</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>            gradient -= subGradient;</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>        }</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>    }</div>
</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span><span class="comment"></span> </div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span><span class="comment">    /// Input derivative, calculated according to the equation:</span></div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span><span class="comment">    /// &lt;br/&gt;</span></div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span><span class="comment">    /// \f$ \frac{\partial k(x, y)}{\partial x}</span></div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span><span class="comment">    ///     \frac{\sum_i \exp(w_i) \frac{\partial k_i(x, y)}{\partial x}}</span></div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span><span class="comment">    ///          {\sum_i exp(w_i)} \f$</span></div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span><span class="comment">    /// and summed up over all elements of the second batch</span></div>
<div class="foldopen" id="foldopen00241" data-start="{" data-end="}">
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#a78299d1e57af5a48afab449ad248f80f">  241</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_normalized_kernel.html#a78299d1e57af5a48afab449ad248f80f">weightedInputDerivative</a>( </div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>        <a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#af9c5f353cd3f34beff84c52229cf71d7">ConstBatchInputReference</a> <span class="keyword">const</span>&amp; batchX1, </div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>        <a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#af9c5f353cd3f34beff84c52229cf71d7">ConstBatchInputReference</a> <span class="keyword">const</span>&amp; batchX2, </div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>        RealMatrix <span class="keyword">const</span>&amp; coefficientsX2,</div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>        <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a> <span class="keyword">const</span>&amp; state, </div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>        <a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#ae982a2e874a2d918533a8f663faf0ecb">BatchInputType</a>&amp; gradient</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>    )<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>        InternalState <span class="keyword">const</span>&amp; s = state.<a class="code hl_function" href="structshark_1_1_state.html#a9847e65e063245c6b02371c8b84f8da3" title="Safely downcast State to it&#39;s derived type.">toState</a>&lt;InternalState&gt;();</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>        std::size_t sizeX1 = <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(batchX1);</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>        </div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>        RealMatrix weights = coefficientsX2 / sqrt(outer_prod(s.kxx,s.kyy));</div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>        </div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>        <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#af534a7a45f73baab879c2f0bfb75f00a" title="Calculates the derivative of the inputs X1 (only x1!).">weightedInputDerivative</a>(batchX1,batchX2,weights,*s.stateKxy,gradient);</div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>        </div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>        noalias(weights) *= s.kxy;</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>        RealVector wx = sum(as_rows(weights))/s.kxx;</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>        </div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span>        <span class="comment">//the following mess could be made easier with an interface like </span></div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>        <span class="comment">//m_base-&gt;weightedInputDerivative(batchX1,wx,s.statekxx,subGradient);</span></div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>        <span class="comment">//(calculating the weighted input derivative of k(x_i,x_i)</span></div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>        RealMatrix subGradient;</div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>        <a class="code hl_typedef" href="classshark_1_1_normalized_kernel.html#ae982a2e874a2d918533a8f663faf0ecb">BatchInputType</a> singleBatch = <a class="code hl_struct" href="structshark_1_1_batch.html" title="class which helps using different batch types">Batch&lt;InputType&gt;::createBatch</a>(<a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(batchX1,0));</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>        RealMatrix coeff(1,1);</div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != sizeX1; ++i){</div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>            <a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(singleBatch,0) = <a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>(batchX1,i);</div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>            coeff(0,0) = wx(i);</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>            <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#af534a7a45f73baab879c2f0bfb75f00a" title="Calculates the derivative of the inputs X1 (only x1!).">weightedInputDerivative</a>(singleBatch,singleBatch,coeff,*s.stateKxx[i],subGradient);</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>            noalias(row(gradient,i)) -= row(subGradient,0);</div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>        }</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>    }</div>
</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span> </div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span><span class="keyword">protected</span>:<span class="comment"></span></div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span><span class="comment">    /// kernel to normalize</span></div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno"><a class="line" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d">  274</a></span><span class="comment"></span>    <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a>* <a class="code hl_variable" href="classshark_1_1_normalized_kernel.html#ad7a155510b5d36a093edf6d0afd7c67d" title="kernel to normalize">m_base</a>;</div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span>};</div>
</div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span> </div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno"><a class="line" href="namespaceshark.html#a4a0643d3202bdedd0cb94d45be2936f3">  277</a></span><span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_normalized_kernel.html" title="Normalized version of a kernel function.">NormalizedKernel&lt;&gt;</a> <a class="code hl_typedef" href="namespaceshark.html#a4a0643d3202bdedd0cb94d45be2936f3">DenseNormalizedKernel</a>;</div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"><a class="line" href="namespaceshark.html#a8d0c4a3e744bf840fad2dea6703d25c1">  278</a></span><span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_normalized_kernel.html" title="Normalized version of a kernel function.">NormalizedKernel&lt;CompressedRealVector&gt;</a> <a class="code hl_typedef" href="namespaceshark.html#a8d0c4a3e744bf840fad2dea6703d25c1">CompressedNormalizedKernel</a>;</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span> </div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span> </div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span>}</div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span><span class="preprocessor">#endif</span></div>
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